Bio
Born and raised in a large extended family from Indianapolis, Indiana, Kynon Jade Benjamin is proud to be the first doctor in his family. Dr. Benjamin earned his GED with the support of his family before moving on to Indiana University–Purdue University Indianapolis (IUPUI). At IUPUI, Dr. Benjamin completed his work study at a neuroscience research laboratory, which started his scientific research journey. In his predoctoral studies, Dr. Benjamin designed and implemented drug delivery and drug development assays as well as developed bioinformatic pipelines for differential expression analysis for Angelman syndrome - a neurodevelopmental disorder. In his subsequent postdoctoral fellowship at the Lieber Institute for Brain Development and Johns Hopkins University School of Medicine, he developed computational pipelines for large-scale transcriptional (bulk and single-cell), genetic, and functional associations analyses in postmortem brain and brain (cerebral and striatal) organoids.
The primary goal of Dr. Benjamin’s research is to improve therapeutics for under-research communities (i.e., personalized medicine) via investigating ancestry differences for neurological disorders in relevant tissues. To this end, he uses and develops computational tools to examine the role of ancestry in the brain. In addition to this, Dr. Benjamin has established collaborations to use computational tools to address hypothesize driven question with publically available single-cell and bulk tissues.
Throughout his research career, Dr. Benjamin’s experiences have reinforced the critical need for diversity and creating inclusive spaces. As such, he has worked to provide mentorship and representation as well as advocate for opportunities for other underrepresented minorities.
Education
| Institute / University | Location | Degree/Field | Completion Year |
|---|---|---|---|
| Lieber Institute for Brain Development | Baltimore, MD | Postdoctoral Fellow in Computational Genetics | Current |
| Johns Hopkins University School of Medicine | Baltimore, MD | Postdoctoral Fellow in Computational Genetics | Current |
| Texas A&M University | College Station, TX | Ph.D. in Genetics | August 2017 |
| Rensselaer Polytechnic Institute | Troy, NY | B.S. in Biomedical Engineering | May 2012 |
| Indiana University, Purdue University, Indianapolis | Indianapolis, IN | Biomedical Engineering (transferred) | May 2010 |
Research Interests
Neurological research in African Americans to reduce health disparities (K99)
Project: Comprehensive Computational Analysis of Genetic and Regulatory Differences Between Individuals With African and European Ancestries Across Four Brain Regions. Funding from National Institute on Minority Health and Health Disparities (K99 MD0169640).
MOSAIC
Scientific Society: The
Association of American Medical Colleges (AAMC).
In neuroscience and genomics, individuals with recent African ancestry (AA) account for less than 5% of large-scale research cohorts for brain disorders but are 20% more likely to experience a major mental health crisis. Furthermore, divergent responses to antipsychotics between AA and European ancestry (EA) have been linked to genetic differences. Understanding these genetic and/or regulatory differences between AA and EA in the human brain, is essential to the development of effective neurotherapeutics and potentially could decrease health disparities for neurological disorders.
- Identify and characterize ancestry-related expression
differences in postmortem brain from AA and EA
individuals.
Rationale: Preliminary results revealed significant transcriptional changes across the four brain regions in neurotypical controls with admixed AA associated with genetic ancestry. Hypothesis: Genetic variations influence ancestry-related transcriptional changes via alternative isoform usage. 1) Differently expressed (DE) genomic features (genes, transcripts, exons, and junctions) associated with genetic ancestry will be identified and characterized within admixed AA. 2) eQTL analysis will be preformed to identify genetic variants interacting with genetic ancestry and expression; and 3) The contribution of these genetic variants on ancestry-related expression differences will be determined.
- Identify and characterize of ancestry-related epigenetic
differences in the brain.
Rationale: Transgenerational stress has been shown to affect health outcomes. Due to past policies and practices, individuals of AA may be differentially exposed to extreme stresses across generations, which are identified as potential risk factors for common disorders. Hypothesis: Epigenetic differences drive ancestry-related expression differences in the brain. 1) Differentially methylated regions (DMRs) and genetic variation associated with DNAm between ancestries will be identified and characterized using BrainSeq Consortium publicly available expression, genetic, and DNA methylation for the caudate nucleus (n=400) and dorsolateral prefrontal cortex (n=378). 2) The contribution of these DMRs and DNAm on ancestry-related expression differences will be determined.
- Evaluate ancestry-related genetic and epigenetic
correlations with complex traits in the post-mortem
brain.
Rationale: The integration of genomic information with complex traits has been used to improve our understanding of disease mechanism and prioritized potential therapeutic targets. Genetic differences have been linked to divergent responses to antipsychotics, suggesting genetic background is important to understanding individual disease susceptibility. Hypothesis: Epigenetic differences drive ancestry-specific complex trait associations in the brain. 1) Causal variants impacting complex trains with ancestry-related expression and DNAm will be identified and characterized. 2) Transfer learning will be applied to improve causal variant detection for AA.
Software development
dRFEtools:
dynamic Recursive Feature Elimination
Technology advances have generated larger ’OMICs datasets
with applications for machine learning. Even so, sample
availability results in smaller sample sizes compared to
features. Dynamic recursive feature elimination (RFE)
provides a flexible feature elimination framework to tackle
this problem. dRFEtools provides an interpretable and
flexible tool to gain biological insights from ’OMICs data
using machine learning.
Authors: Kynon Jade Benjamin, Apuã Paquola, and Tarun
Katipalli
Pre-print DOI:
https://doi.org/10.1101/2022.07.27.501227.
Collaborations
Project: Angiotensin II receptors in the
human lung
Rationale: The renin-angiotensin system is one of
the most well characterized integrative pathways in humans
and is known to contribute to a variety of common disorders
such as hypertension, chronic renal disease, and heart
failure. The wide availability of agents targeting this
pathway has led to an expansion of its clinical spectrum to
lung disorders. Despite this widespread interest, specific
localization of this receptor family in the vertebrate lung
is limited. One reason is due to the general imprecision of
the available antibody reagents.
Goal: Use publicly available single-cell
and bulk RNA-sequencing to identify and characterize
patterns of angiotensin II receptor expression in the lung
at different ages.
Select Publications
Benjamin, KJM, Chen, Q, Jaffe, AE, Stolz, JM, Collado-Torres, L, Huuki-Myers, LA, Burke, EE, Arora, R, Feltrin, AS, Barbosa, AR, Radulescu, E, Pergola, G, Shin, JH, Ulrich, WS, Deep-Soboslay, A, Tao, R, the BrainSeq Consortium, Hyde, TM, Kleinman, JE, Erwin, JA, Weinberger, DR, and Apuã CM Paquola. ``Analysis of the caudate nucleus transcriptome in individuals with schizophrenia highlights effects of antipsychotics and novel risk genes’’. Nature Neuroscience. 2022. In Press. DOI: https://doi.org/10.1101/2020.11.18.20230540.
Benjamin, KJM+, Arora, R+, D’Ignazio, L, Hyde, TM, Kleinman, JE, Weinberger, DR, Paquola, ACM, and Jennifer A Erwin. ``Transcriptional and genetic sex differences for schizophrenia across the dorsolateral prefrontal cortex, hippocampus, and caudate nucleus’’. [preprint]. 2022. DOI: https://doi.org/10.1101/2022.09.30.22280452.
Sawada, T, Benjamin, KJM, Brandtjen, AC, Tietze, E, Allen, SJ, Paquola, ACM, Kleinman, JE, Hyde, TM, and Jennifer A Erwin. ``Generation of four postmortem dura-derived iPS cell lines from four control individuals with genotypic and brain-region-specific transcrptomic data available through the BrainSEQ consortium’’. Stem Cell Research. 2020. PMID: 32446240.
Past Funding
Grant: T32 MH015330, 06/01/2019-05/31/2021
Funding Source: NIH/NIMH
Role: Postdoctoral Fellow
This Interdisciplinary Training Program in Psychiatry and
Neuroscience provides postdoctoral research training in
areas relevant to the neurobiological bases of mental
disorders. The close interaction with the Lieber Institute
provides additional training opportunities with large-scale
postmortem genetic and regulatory studies.
Grant: CVM Advanced Developmental Training Travel Award,
05/01/2015-08/31/2015
Funding Source: Texas A&M University College of
Veterinary Medicine & Biomedical Sciences
Role: PI
This is a travel award to attend the Cold Spring Harbor
Laboratory Drosophila Neurobiology Course
($2500).
Grant: Great Lakes National STEM Scholarship,
07/01/2014
Funding Source: Great Lakes
Role: PI
This is a scholarship program for individuals in the STEM
(science, technology, engineering, and mathematics) open to
high school students, college students, and graduate
students ($2500).
Grant: CVM Graduate Student Research Trainee Grant,
05/01/2014
Funding Source: Texas A&M University College of
Veterinary Medicine & Biomedical Sciences
Role: PI
This is a graduate student research trainee grant for
research and supplies to generate preliminary data
($5000).
Grant: WSGI Graduate Traineeship,
09/01/2013-08/31/2014
Funding Source: Texas A&M Institute for Genome Sciences
and Society (WSGI)
Role: PI
This is a competitive graduate traineeship that supports
half of salary for computational analysis of 50 idiopathic
Angelman Syndrome patients.